Instructors
Meet our experienced team of instructors.
Instructors
Benjamin Afflerbach
Research Associate, University of Wisconsin-Madison
Benjamin Afflerbach received a B.S in mechanical engineering from Texas A&M University in 2014. He then obtained a Ph.D. in materials science and engineering from the University of Wisconsin-Madison in 2021. Afflerbach is currently a Research Associate in the Materials Science and Engineering department at the University of Wisconsin-Madison. His research is focused on using materials informatics to supplement traditional materials science research, with the focus of this research being on discovery and characterization of metallic glasses. In addition to personal research, he is heavily involved in management of the undergraduate research group the Informatics Skunkworks through which he has mentored multiple undergraduate research groups, developed educational materials for onboarding undergraduate researchers, and built community infrastructure to help grow the ML community.
Ankit Agrawal
Research Professor, Northwestern University
Ankit Agrawal is a research professor in the Department of Electrical and Computer Engineering at Northwestern University, USA. He specializes in interdisciplinary artificial intelligence (AI) and big data analytics via high performance data mining, based on a coherent integration of high-performance computing and data mining to develop customized AI solutions for big data problems with real-world impact. His research has contributed to large-scale data-driven discoveries in various scientific and engineering disciplines, such as materials science, healthcare, social media, and bioinformatics. He has co-authored 150+ peer-reviewed publications, co-developed and released 15+ software, delivered 50+ invited/keynote talks at major conferences, universities, and companies all over the world, been on program committees of 40+ conferences/workshops, and served as a PI/Co-PI on 15+ sponsored projects funded by various US federal agencies (e.g., NSF, DOE, AFOSR, NIST, DARPA, DLA) as well as industry (e.g., Toyota Motor Corporation Japan). He is one of the few computer scientists who are actively introducing AI and advanced data science techniques in the field of materials science and has successfully led several large-scale materials informatics projects. As an example, he is co-leading the AI group at the Center for Hierarchical Materials Design (CHiMaD), which is a $60 million NIST-sponsored center of excellence. He is also serving as the editor-in-chief of Computers, Materials & Continua.
Raymundo Arróyave
Professor and Presidential Impact Fellow, Department of Materials Science and Engineering, Texas A&M University
Raymundo Arróyave is a founding faculty member of the Texas A&M Department of Materials Science and Engineering, after having joined the university’s Mechanical Engineering Department in 2006. His main areas of interest include: 1) computational thermodynamics and phase stability in structural and functional materials; 2) kinetic processes and microstructure evolution simulation; 3) multi-scale computational materials science; 4) simulation-assisted materials design; 5) machine learning and artificial intelligence-enabled materials discovery; and 6) integrated computational materials engineering in additive manufacturing. He has worked on a number of materials classes including structural and functional alloys, thin films, and nanomaterials with applications in energy and transportation. His awards and honors include the NSF CAREER Award and the TMS 2019 Brimacombe Medal, and he has authored or co-authored more than 170 peer-reviewed articles and conference proceedings.
David Blondheim, Jr.
Technical Advisor: Advanced Manufacturing Engineering and Analytics, Mercury Marine, a division of Brunswick Corporation
David Blondheim, Jr. graduated in 2004 with his B.S. in Mechanical Engineering from Michigan Technological University. He began his engineering career at a CNC machine job shop. While working full time, he became a Professional Engineer (PE), completed his MBA from UW-Oshkosh in 2008 and obtained his M.S. in Industrial Engineering from Purdue University in 2012. After nine years of progressive experience in engineering for machining, he entered the die cast industry with Mercury Marine in 2013. Blondheim serves as the engineering manager within the aluminum foundry and Technical Advisor leading IIoT/Connected Operations initiatives throughout Mercury’s different manufacturing plants. Blondheim is currently a Ph.D. candidate in Systems Engineering at Colorado State University working on his dissertation of improving die casting manufacturing system with machine learning.
Sayan Ghosh
Lead Engineer, General Electric Research
Sayan Ghosh is lead engineer and works with probabilistic machine learning, design and optimization team at GE Research in New York, USA. He has over 10 years’ experience in probabilistic machine learning, digital twin, hybrid-physics modeling, uncertainty quantification and management, inverse modeling, reliability and risk analysis, multidisciplinary design, optimization, etc. At GE Research, he is leading multiple projects involving development and application of advanced probabilistic and machine-learning methods supporting various product lines, new product and technology integration, maintenance, services, and operations for GE business involving Aviation, Power, Additive, Renewables, etc. He has received his B.Tech. from IIT Kharagpur, M.S. from Iowa State University and Ph.D. from the Georgia Institute of Technology in Aerospace Engineer.
Vipul Gupta
Senior Materials Scientist, General Electric Research
Vipul Gupta is a senior materials scientist in the Materials Organization at the GE Research, Niskayuna, New York, USA. Dr. Gupta’s research focuses on understanding mechanical behavior of structural aluminum alloys, ceramic matrix composites, high-temperature nickel and cobalt-based superalloys subject to harsh environment and stress conditions. In recent years, he has led projects on metal additive manufacturing, including new additive alloy development, process parameter optimization, and high-throughput testing and characterization. He is also passionate about artificial intelligence (AI) and machine learning (M) for Materials; and has been working toward implementing AI/ML for alloy design, additive process optimization, material property predictions; and development of federated big data storage, visualization and analytics platform for additive manufacturing. He has co-authored more than 20 peer-reviewed publications and have delivered over 25 presentations at the international conferences, workshops, and university seminars.
Ryan Jacobs
Research Scientist, University of Wisconsin-Madison
Ryan Jacobs is currently a Research Scientist in the Department of Materials Science and Engineering at the University of Wisconsin-Madison. His work focuses on using atomistic modeling, data science, and machine learning (materials informatics) methods to understand the structure and properties of materials at the atomic scale to discover and design novel material compounds for a range of technological applications. His main research application areas of interest comprise materials for energy technology, such as solid oxide and protonic fuel cells, batteries, and solar photovoltaics. Another main thrust of his research is the investigation of surface electronic and thermodynamic properties of metals and oxides used as electron emission cathodes.
Benji Maruyama
Principal Materials Research Engineer
Autonomous Research Lead
U.S. Air Force Research Laboratory Materials and Manufacturing Directorate
Benji Maruyama is a principal materials research engineer in the U.S. Air Force Research Laboratory Materials and Manufacturing Directorate and the autonomous materials lead and ACT3 (Autonomous Capabilities Team 3) liaison. His focus area is the synthesis and processing science of carbon nanotubes using ARES™ which is the first fully Autonomous Research (ARES) Robot for materials. Maruyama’s interests include the research process itself, for which he promotes Moore’s Law for the speed of research. He is also the point of contact for carbon materials for the AFRL Materials & Manufacturing Directorate. His materials interests include carbon nanomaterials, energy storage, flexible-hybrid materials and processes, field emission, carbon, polymer and metal matrix composites, imaging of complex 3D microstructures and AI/Machine Learning. He is currently involved in the study of the origins of chiral growth for carbon nanotubes, defect engineering for low dimensional materials, catalysis and autonomous experimentation.
Bryce Meredig
Travertine Labs LLC
Bryce Meredig is cofounder and chief science officer of Citrine Informatics, a materials informatics platform company, where he leads the External Research Department (ERD). ERD conducts publishable research with collaborators in academia, government, and industry. Meredig's research interests include the development and validation of physics-aware machine learning methods specific to applications in materials science and chemistry; integration of physics-based simulations with machine learning; trust and interpretability of machine learning models of physical phenomena; and data infrastructure for materials science. Meredig received his Ph.D. from Northwestern University and BAS and MBA from Stanford University.
Dane Morgan
Harvey D. Spangler Professor, Department of Materials Science and Engineering, University of Wisconsin-Madison
Dane Morgan is the Harvey D. Spangler Professor of Engineering in the department of Materials Science and Engineering at the University of Wisconsin-Madison. His work combines thermostatistics, thermokinetics, and informatics analysis with atomic scale calculations to understand and predict materials properties. Morgan is presently training or has graduated/trained over 70 graduate students and postdoctoral researchers and led the Informatics Skunkworks, which has helped engage over 350 undergraduates at the interface of data science and science and engineering. He has received multiple teaching and research awards and has published over 350 papers in materials science.
Kristofer Reyes
Assistant Professor, Department of Materials Design and Innovation, University at Buffalo – The State University of New York
Kristofer G. Reyes is an assistant professor in the Department of Materials Design and Innovation, University at Buffalo. He applies machine learning and artificial intelligence methods to problems in materials science. He is particularly interested in making these methods relevant in the regime of sparse and noisy data, a regime in which much of materials science research is conducted. A major thrust of his work is autonomous science, which uses robot scientists to plan and execute experiments, and scientific machine learning to posit hypotheses. In this area, he develops methods for designing optimal experiments based on a limited set of information, algorithms for the characterization of rich and complex data resulting from experiments, and techniques for learning and leveraging physics-based and domain-expert knowledge. He received his Ph.D. in Applied Mathematics from the University of Michigan, where he modeled the synthesis of nanostructures grown by multiphase methods. His postdoctoral training was at the Department of Operations Research and Financial Engineering at Princeton University. There, he studied stochastic optimization and machine learning problems related to materials development and optimization.
For More Information
For more information about this course, please contact:
TMS Meeting Services
5700 Corporate Drive Suite 750
Pittsburgh, PA 15237
Telephone:
U.S. and Canada Only: 1-800-759-4867
Other Countries: 1-724-776-9000
Fax: 1-724-776-3770